001030128 001__ 1030128
001030128 005__ 20250203133158.0
001030128 0247_ $$2doi$$a10.1103/PhysRevResearch.6.033264
001030128 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-05234
001030128 0247_ $$2WOS$$aWOS:001310522300005
001030128 037__ $$aFZJ-2024-05234
001030128 082__ $$a530
001030128 1001_ $$0P:(DE-Juel1)176960$$aDick, Michael$$b0$$eCorresponding author$$ufzj
001030128 245__ $$aLinking network- and neuron-level correlations by renormalized field theory
001030128 260__ $$aCollege Park, MD$$bAPS$$c2024
001030128 3367_ $$2DRIVER$$aarticle
001030128 3367_ $$2DataCite$$aOutput Types/Journal article
001030128 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1730190085_31472
001030128 3367_ $$2BibTeX$$aARTICLE
001030128 3367_ $$2ORCID$$aJOURNAL_ARTICLE
001030128 3367_ $$00$$2EndNote$$aJournal Article
001030128 520__ $$aIt is frequently hypothesized that cortical networks operate close to a critical point. Advantages of criticality include rich dynamics well suited for computation and critical slowing down, which may offer a mechanism for dynamic memory. However, mean-field approximations, while versatile and popular, inherently neglect the fluctuations responsible for such critical dynamics. Thus, a renormalized theory is necessary. We consider the Sompolinsky-Crisanti-Sommers model which displays a well studied chaotic as well as a magnetic transition. Based on the analog of a quantum effective action, we derive self-consistency equations for the first two renormalized Greens functions. Their self-consistent solution reveals a coupling between the population level activity and single neuron heterogeneity. The quantitative theory explains the population autocorrelation function, the single-unit autocorrelation function with its multiple temporal scales, and cross correlations.
001030128 536__ $$0G:(DE-HGF)POF4-5232$$a5232 - Computational Principles (POF4-523)$$cPOF4-523$$fPOF IV$$x0
001030128 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x1
001030128 536__ $$0G:(DE-Juel-1)BMBF-01IS19077A$$aRenormalizedFlows - Transparent Deep Learning with Renormalized Flows (BMBF-01IS19077A)$$cBMBF-01IS19077A$$x2
001030128 536__ $$0G:(GEPRIS)491111487$$aDFG project G:(GEPRIS)491111487 - Open-Access-Publikationskosten / 2022 - 2024 / Forschungszentrum Jülich (OAPKFZJ) (491111487)$$c491111487$$x3
001030128 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
001030128 7001_ $$0P:(DE-HGF)0$$aMeegen, Alexander van$$b1
001030128 7001_ $$0P:(DE-Juel1)144806$$aHelias, Moritz$$b2$$ufzj
001030128 773__ $$0PERI:(DE-600)3004165-X$$a10.1103/PhysRevResearch.6.033264$$gVol. 6, no. 3, p. 033264$$n3$$p033264$$tPhysical review research$$v6$$x2643-1564$$y2024
001030128 8564_ $$uhttps://juser.fz-juelich.de/record/1030128/files/INV_24_AUG_014730.pdf
001030128 8564_ $$uhttps://juser.fz-juelich.de/record/1030128/files/INV_24_AUG_014730.gif?subformat=icon$$xicon
001030128 8564_ $$uhttps://juser.fz-juelich.de/record/1030128/files/INV_24_AUG_014730.jpg?subformat=icon-1440$$xicon-1440
001030128 8564_ $$uhttps://juser.fz-juelich.de/record/1030128/files/INV_24_AUG_014730.jpg?subformat=icon-180$$xicon-180
001030128 8564_ $$uhttps://juser.fz-juelich.de/record/1030128/files/INV_24_AUG_014730.jpg?subformat=icon-640$$xicon-640
001030128 8564_ $$uhttps://juser.fz-juelich.de/record/1030128/files/PhysRevResearch.6.033264.pdf$$yOpenAccess
001030128 8564_ $$uhttps://juser.fz-juelich.de/record/1030128/files/PhysRevResearch.6.033264.gif?subformat=icon$$xicon$$yOpenAccess
001030128 8564_ $$uhttps://juser.fz-juelich.de/record/1030128/files/PhysRevResearch.6.033264.jpg?subformat=icon-1440$$xicon-1440$$yOpenAccess
001030128 8564_ $$uhttps://juser.fz-juelich.de/record/1030128/files/PhysRevResearch.6.033264.jpg?subformat=icon-180$$xicon-180$$yOpenAccess
001030128 8564_ $$uhttps://juser.fz-juelich.de/record/1030128/files/PhysRevResearch.6.033264.jpg?subformat=icon-640$$xicon-640$$yOpenAccess
001030128 8767_ $$8INV/24/AUG/014730$$92024-08-16$$d2024-08-19$$eAPC$$jZahlung angewiesen$$zUSD 2755
001030128 909CO $$ooai:juser.fz-juelich.de:1030128$$pdnbdelivery$$popenCost$$pec_fundedresources$$pVDB$$pdriver$$pOpenAPC$$popen_access$$popenaire
001030128 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176960$$aForschungszentrum Jülich$$b0$$kFZJ
001030128 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)176960$$a Department of Computer Science 3 - Software Engineering, RWTH Aachen University, 52074 Aachen, Germany$$b0
001030128 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Institute of Zoology, University of Cologne, 50674 Cologne, Germany$$b1
001030128 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-HGF)0$$aForschungszentrum Jülich$$b1$$kFZJ
001030128 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144806$$aForschungszentrum Jülich$$b2$$kFZJ
001030128 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)144806$$a Department of Physics, Faculty 1, RWTH Aachen University, 52065 Aachen, Germany$$b2
001030128 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5232$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
001030128 9141_ $$y2024
001030128 915pc $$0PC:(DE-HGF)0000$$2APC$$aAPC keys set
001030128 915pc $$0PC:(DE-HGF)0003$$2APC$$aDOAJ Journal
001030128 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
001030128 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2023-10-27
001030128 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001030128 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2023-10-27
001030128 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2025-01-02
001030128 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2025-01-02
001030128 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2024-02-07T08:08:02Z
001030128 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2024-02-07T08:08:02Z
001030128 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Anonymous peer review$$d2024-02-07T08:08:02Z
001030128 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2025-01-02
001030128 915__ $$0StatID:(DE-HGF)0112$$2StatID$$aWoS$$bEmerging Sources Citation Index$$d2025-01-02
001030128 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2025-01-02
001030128 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lComputational and Systems Neuroscience$$x0
001030128 9201_ $$0I:(DE-Juel1)PGI-1-20110106$$kPGI-1$$lQuanten-Theorie der Materialien$$x1
001030128 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x2
001030128 980__ $$ajournal
001030128 980__ $$aVDB
001030128 980__ $$aUNRESTRICTED
001030128 980__ $$aI:(DE-Juel1)IAS-6-20130828
001030128 980__ $$aI:(DE-Juel1)PGI-1-20110106
001030128 980__ $$aI:(DE-Juel1)INM-6-20090406
001030128 980__ $$aAPC
001030128 9801_ $$aAPC
001030128 9801_ $$aFullTexts